Devices and systems for real-time recognition of restoration of spontaneous circulation (rosc) in cardio-pulmonary resuscitation (cpr) process

ABSTRACT

This disclosure relates to methods, devices and systems for real-time recognition of restoration of spontaneous circulation (ROSC) in the cardio-pulmonary resuscitation (CPR) process. Recognition mechanisms in both time domain and frequency domain are provided for the ROSC recognition, where the time-domain recognition logic may detect the ROSC by recognizing envelope features of sampled signals in the time domain, and the frequency-domain recognition logic may detect the ROSC by recognizing spectral peaks at different frequency points continuously or significant variations of amplitude of spectral peaks in the frequency spectrum.

CROSS-REFERENCE

This application claims benefit and advantages of Chinese PatentApplication No. 201310685817.2, filed Dec. 16, 2013, which is herebyincorporated by reference.

TECHNICAL FIELD

This disclosure generally relates to recognition of restoration ofspontaneous circulation (ROSC), and in particular relates to devices forreal-time recognition of ROSC in the cardio-pulmonary resuscitation(CPR) process, ROSC recognition and post-ROSC circulation qualityevaluation systems, and ROSC feedback systems in the CPR process.

BACKGROUND

Cardiovascular disease, the major manifestation of which is suddencardiac death (SCD), has become the top cause of death. Cardiopulmonaryresuscitation (CPR) is universally acknowledged to be the most effectivemethod to rescue patients from cardiac arrest. Given to the fact thatevidence has accumulated that even short interruptions in CPR areharmful, the 2010 American Heart Association Guidelines for CPR andEmergency Cardiovascular Care puts an emphasis on immediately resumingCPR after shock delivery rather than evaluating the restoration ofspontaneous circulation (ROSC). However, this recommendation neglectsthe problem of interference between the spontaneous circulation and thechest compression in patients who had ROSC, which may disturbhemodynamics and exacerbate the damage to the heart that could causeheart arrest. There is thus a need to develop a rapid and exactrecognition system of ROSC in the CPR process to avoid this problem.

SUMMARY OF THIS DISCLOSURE

Spontaneous circulation recognition systems can be established based onthe theory of pulse oximetry waveform analysis, which would make it easyto recognize ROSC during CPR. These systems may help doctors determinewhen to stop CPR in time and increase CPR effectiveness.

In this disclosure, for the purpose of assisting in a doctor's clinicaldecisions, ROSC in the CPR process can be recognized based on arterialpulse oximetry technology and in combination with clinical physiologicalcharacteristics and digital signal processing methods.

In one aspect, an ROSC recognition device for real-time recognition ofROSC in the CPR process can include a signal acquisition apparatus and asignal analysis apparatus. The signal acquisition apparatus can be usedto acquire pulse oximetry waveform signals of a patient. The signalanalysis apparatus can be used to analyze these signals to determinewhether there is ROSC in the CPR process in real time.

In some embodiments, the signal analysis apparatus can includetime-domain recognition logic and/or frequency-domain recognition logic.The time-domain recognition logic can determine whether there is ROSC inthe CPR process by detecting envelope features of the signals in thetime domain, and the frequency-domain recognition logic can determinewhether there is ROSC in the CPR process by detecting time-varyingfeatures of spectral peaks of the signals in the frequency domain.

In some embodiments, the time-domain recognition logic can determinethat there is ROSC when continuous and regular envelope features arerecognized.

In some embodiments, the frequency-domain recognition logic candetermine that there is ROSC when spectral peaks are recognizedcontinuously at different frequencies or significant amplitude change isrecognized for spectral peaks within a certain period.

In some embodiments, the signal acquisition apparatus may acquire thepulse oximetry waveform signals of the patient by red light and/orinfrared light.

In another aspect, an ROSC recognition and post-ROSC circulation qualityevaluation system can include the above-described ROSC recognitiondevice and a post-ROSC circulation quality evaluation apparatus forevaluating post-ROSC circulation quality.

In some embodiments, the post-ROSC circulation quality evaluationapparatus can evaluate the post-ROSC circulation quality based onvariations of cardiac stroke volume within a certain period.

In some embodiments, after the ROSC recognition device has determinedthere is ROSC, the post-ROSC circulation quality evaluation apparatuscan calculate area characteristics of AC components of pulse signalssampled by the ROSC recognition device to evaluate the stroke volumevariations under spontaneous cardiac rhythm to reflect the post-ROSCdynamic quality.

In some embodiments, the post-ROSC circulation quality evaluationapparatus can evaluate the post-ROSC circulation quality based onvariations of pulse rate.

In still another aspect, an ROSC feedback system in a CPR process caninclude an ROSC recognition and post-ROSC circulation quality evaluationapparatus and a CPR apparatus. The ROSC recognition and post-ROSCcirculation quality evaluation apparatus may be used for real-timerecognition of ROSC in the CPR process and for post-ROSC circulationquality evaluation. The CPR apparatus may provide compression output toa patient. Upon detection of the ROSC, the ROSC recognition andpost-ROSC circulation quality evaluation apparatus can control the CPRapparatus to stop the compression output, and can start the post-ROSCcirculation quality evaluation. When the post-ROSC circulation qualityis evaluated to be unstable, the ROSC recognition and post-ROSCcirculation quality evaluation apparatus can control the CPR apparatusto restart the compression output and can restart the real-timerecognition of ROSC.

In some embodiments, the ROSC recognition and post-ROSC circulationquality evaluation apparatus can include time-domain recognition logicand/or frequency-domain recognition logic. The time-domain recognitionlogic can determine whether there is ROSC in the CPR process bydetecting time envelope features of sampled signals in the time domain,and the frequency-domain recognition logic can determine whether thereis ROSC in the CPR process by detecting time-varying features ofspectral peaks of the sampled signals in the frequency domain.

In some embodiments, the time-domain recognition logic can determinethat there is ROSC when continuous and regular envelope features arerecognized.

In some embodiments, the frequency-domain recognition logic candetermine that there is ROSC when spectral peaks are recognizedcontinuously at different frequencies or significant amplitude change isrecognized for spectral peaks within a certain period.

In yet another aspect, an ROSC feedback system in a CPR process caninclude an ROSC recognition and post-ROSC circulation quality evaluationapparatus, a CPR apparatus and a CPR quality evaluation apparatus. TheROSC recognition and post-ROSC circulation quality evaluation apparatusmay be used for real-time recognition of ROSC in the CPR process and forpost-ROSC circulation quality evaluation. The CPR apparatus may providecompression output to a patient. The CPR quality evaluation apparatusmay be used to evaluate CPR quality. Upon detection of the ROSC, theROSC recognition and post-ROSC circulation quality evaluation apparatuscan control the CPR apparatus to stop the compression output, and canstart the post-ROSC circulation quality evaluation. When the post-ROSCcirculation quality is evaluated to be unstable, the ROSC recognitionand post-ROSC circulation quality evaluation apparatus can control theCPR apparatus to restart the compression output, and can restart thereal-time recognition of ROSC. While the CPR apparatus is providing thecompression output, the CPR apparatus can interact with the CPR qualityevaluation apparatus so that the CPR quality evaluation apparatus canrecognize the CPR compression state and provide feedback to the CPRapparatus to achieve an optimal compression output.

In some embodiments, the ROSC recognition and post-ROSC circulationquality evaluation apparatus can include time-domain recognition logicand/or frequency-domain recognition logic. The time-domain recognitionlogic can determine whether there is ROSC in the CPR process bydetecting time envelope features of sampled signals in the time domain,and the frequency-domain recognition logic can determine whether thereis ROSC in the CPR process by detecting time-varying features ofspectral peaks of the sampled signals in the frequency domain.

In some embodiments, the time-domain recognition logic can determinethat there is ROSC when continuous and regular envelope features arerecognized.

In some embodiments, the frequency-domain recognition logic candetermine that there is ROSC when spectral peaks are recognizedcontinuously at different frequencies or significant amplitude change isrecognized for spectral peaks within a certain period.

In still another aspect, an ROSC recognition device without externalcompression after defibrillation can include a signal acquisition deviceand a signal analysis device. The signal acquisition apparatus can beused to acquire pulse wave signals of a patient. The signal analysisapparatus can be used to analyze these signals to determine whetherthere is ROSC after defibrillation in real time.

In some embodiments, the signal acquisition apparatus can acquire thepulse wave signals of the patient by red light and/or infrared light.

In some embodiments, the signal analysis apparatus can perform real-timefiltering on the signals by a band pass filter before analysis toeliminate noise interference beyond the physiological frequency band.

In some embodiments, the signal analysis apparatus can establish asliding time window for the signals, and then determine whether there isa pulse feature within the sliding time window.

In some embodiments, the signal analysis apparatus can determine thatthere is ROSC after defibrillation when the pulse wave number in thesliding time window exceeds a threshold number, and the quality of asingle pulse wave exceeds a threshold quality.

In some embodiments, the quality of the single pulse wave can bedetermined by its amplitude, width and shape.

In some embodiments, a time duration of the sliding time window can beadaptively adjusted according to frequencies of the pulse wave.

Through the above-described devices and systems, a doctor can knowwhether a patient has restored his/her spontaneous circulation in acompression interval or even in the continuous compression process, andthus the doctor can make clinical decisions in real time to avoidpotential damage on the circulation system and other systems of thepatient with ROSC by the external compression.

These and other features and advantages of this disclosure can beunderstood more thoroughly according to the following descriptions incombination with the accompanying drawings of respective embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed descriptions of respective embodiments in this disclosurecan be understood better when combined with the following figures, inwhich the same structure is represented by the same reference sign.

FIG. 1 is a schematic diagram for blood oxygen saturation detection;

FIG. 2 is a schematic diagram for signals detected by a blood oxygensaturation sensor;

FIGS. 3 and 4 show two change modes of physiological characteristics ofcardiac motion in a CPR process;

FIG. 5 is a flow chart for an ROSC recognition method afterdefibrillation;

FIG. 6 is a schematic diagram for a mixed waveform of manual compressionsignals and spontaneous circulation signals;

FIGS. 7A-7D are schematic diagrams for mixed waveforms of manualcompression signals and spontaneous circulation signals with a phasedeviation of 0°, 90°, 180° and 270° respectively;

FIG. 8 is a schematic diagram for a mixed waveform of manual compressionsignals and spontaneous circulation signals when spontaneous cardiacrhythm has gradual changes in the process of external compression;

FIGS. 9A-9D are schematic diagrams for mixed waveforms of manualcompression signals and spontaneous circulation signals with a phasedeviation of 0°, 90°, 180° and 270° respectively when spontaneouscardiac rhythm has gradual changes in the process of externalcompression;

FIG. 10 is a flow chart for an exemplary time-domain ROSC recognitionlogic;

FIG. 11 shows some calculation parameters in a time-domain ROSCrecognition logic on a waveform;

FIG. 12 shows an exemplary waveform generated when applying atime-domain ROSC recognition logic to an animal experiment;

FIG. 13 is a schematic diagram for a mixed waveform of manualcompression signals and spontaneous circulation signals when there isinterference on these signals;

FIG. 14 is a schematic diagram for a mixed waveform and a spectraldistribution of manual compression signals and spontaneous circulationsignals when manual compression frequency and spontaneous cardiac rhythmare inconsistent with each other;

FIG. 15 is a schematic diagram for a mixed waveform and a spectraldistribution of manual compression signals and spontaneous circulationsignals when manual compression frequency and spontaneous cardiac rhythmare inconsistent with each other, and manual compression signals andspontaneous cardiac rhythm signals have harmonic spectral components;

FIG. 16 is a schematic diagram for a spectral distribution of manualcompression signals and spontaneous circulation signals when manualcompression frequency and spontaneous cardiac rhythm are basicallyconsistent with each other;

FIG. 17 is a schematic diagram for a spectral distribution of manualcompression signals and spontaneous circulation signals when manualcompression frequency and spontaneous cardiac rhythm are basicallyconsistent with each other, and manual compression signals andspontaneous cardiac rhythm signals have harmonic spectral components;

FIGS. 18-21 show variations of spectral distributions of manualcompression signals and spontaneous circulation signals when applying anexemplary frequency-domain ROSC recognition logic;

FIG. 22 is a schematic diagram for AC components of blood oxygen signalssampled from a patient;

FIG. 23 is a schematic diagram illustrating a drive current regulationof sampled blood oxygen signals;

FIG. 24 shows a quality evaluation for spontaneous cardiac rhythm whentaking cardiac stroke volume as an example;

FIG. 25 shows a quality evaluation for spontaneous cardiac rhythm whentaking pulse rate as an example;

FIG. 26 is a block diagram for an ROSC recognition device for real-timerecognition of ROSC in a CPR process according to an embodiment of thisdisclosure;

FIG. 27 shows an example for a shape factor of the ROSC recognitiondevice of FIG. 26 when it is implemented as a one-parameter medicalequipment;

FIG. 28 is a schematic diagram for exemplary hardware of the ROSCrecognition device shown in FIG. 26;

FIG. 29 is an example circuit diagram for an ROSC recognition sub-boardin the ROSC recognition device of FIG. 28;

FIG. 30 is a block diagram for an ROSC recognition and post-ROSCcirculation quality evaluation system according to an embodiment of thisdisclosure;

FIG. 31 is a schematic diagram for exemplary hardware of the ROSCrecognition and post-ROSC circulation quality evaluation system shown inFIG. 30;

FIG. 32 is a schematic diagram for an ROSC feedback system in a CPRprocess according to an embodiment of this disclosure;

FIG. 33 shows detailed internal interactions of the system shown in FIG.32 during operation;

FIG. 34 shows detailed internal interactions of an ROSC feedback systemin a CPR process during operation according to another embodiment ofthis disclosure; and

FIG. 35 is a block diagram for an ROSC recognition device withoutexternal compression after defibrillation according to an embodiment ofthis disclosure.

DETAILED DESCRIPTION

For better understanding of this disclosure, various embodiments in thefigures are described below.

Blood oxygen saturation detection has been widely used in clinicalpractice. Pulse oximetry waveform (e.g., amplitude and area under thecurve) obtained in such detection may be related to hemodynamic effectssuch as cardiac output, volume condition and peripheral tissueperfusion. It is further discovered that the pulse oximetry waveform canreflect both cardio-pulmonary quality and ROSC characteristics in a CPRprocess. Therefore, some relevant parameters of the pulse oximetrywaveform can be established in this disclosure by using the blood oxygensaturation detection method (e.g., near-infrared light detection), whichmeans that a continuous and non-invasive method for automaticrecognition of ROSC in the CPR process can be established.

Determination of blood oxygen saturation may include two parts, namelyspectrophotometric determination and blood plethysmography. Thespectrophotometric determination can be performed by using red lightwith a wavelength of about 660 nm and infrared light with a wavelengthof about 940 nm. Oxyhemoglobin (HbO₂) has less absorption for 660 nm redlight and more absorption for 940 nm infrared light, while hemoglobin(Hb) has more absorption for 660 nm red light and less absorption for940 nm infrared light. A ratio between the infrared light absorptionintensity and the red light absorption intensity can be calculated so asto determine an oxygenation degree of hemoglobin, namely the bloodoxygen saturation (SaO₂).

FIG. 1 is a schematic diagram for blood oxygen saturation detection. Twolight emitting tubes that emit red light and infrared light respectivelycan be mounted on one side of a probe, while a photoelectric detector(i.e., receiving tube) can be mounted on the other side of the probe,where the photoelectric detector may convert detected red light andinfrared light that penetrate through finger arteries into electricsignals. Skin, muscle, fat, venous blood, pigment and bone may haveconstant absorption coefficients for these two lights. Concentrations ofHbO₂ and Hb in arterial blood may have periodic changes witharteriopalmus, thereby causing periodic changes of signal intensityoutputted by the photoelectric detector. Blood oxygen saturation andpulse rate can then be determined by processing these signals havingperiodic changes.

When determining pulse oximetry saturation, blood perfusion should beprovided. When a light beam transilluminates a peripheral tissue, theextent of attenuation of transilluminated light energy may be related toa cardiac cycle. At the time of systole, peripheral blood volume ishighest, and light absorption intensity reaches a maximum value, whiledetected light energy reaches a minimum value. The situation is reversedat the time of diastole. Variations of light absorption intensity canreflect variations of blood volume. Varying the blood volume can changethe intensity of the transilluminated light energy.

When detecting the light intensity by the photoelectric detector, asmaller value is obtained at the moment of cardiac impulse while alarger value is obtained at the cardiac impulse interval. There may be aD-value between these two values, which can be the light absorptionintensity of pulsating arterial blood. In this way, a light absorptionrate (R) between two wavelengths, which may be in negative correlationwith SaO₂, can be calculated as follows:

R=(AC660/DC660)/(AC940/DC940). Blood oxygen saturation of a patient canthen be calculated according to a standard curve established by data ofnormal volunteers. A formula for calculating oxygen saturation is shownbelow: oxygen saturation%=oxyhemoglobin/(oxyhemoglobin+deoxyhemoglobin)*100%.

In an SaO₂ sensor, two light emitting diodes (LED) that emit about 660nm red light and about 940 nm infrared light respectively may be mountedon one side, while one photoelectric detector may be mounted on anopposite side. Therefore, LEDs should be opened and closed alternativelyso that the photoelectric detector can distinguish the absorptionintensity under different wavelengths. Influences of ambient light ondetection should be eliminated from the transilluminated light at eachwavelength. When the 660 nm/940 nm light penetrates the biologicaltissue, there may be a difference between HbO₂ and Hb in lightabsorption intensity. Absorption at each wavelength can be a function ofskin color, skin structure, iliacus muscle, blood and other tissuespenetrated by the light. The light absorption intensity can beconsidered as a sum of pulsating absorption and non-pulsatingabsorption. FIG. 2 is a schematic diagram for signals detected by theSaO₂ sensor, in which the AC component can be caused by pulsatingarterial blood, while the DC component can be caused by the lightabsorption intensities of non-pulsating arterial blood, venous blood andtissues. Perfusion index PI is a percentage of AC in DC (PI=AC/DC×100%).It can be seen in FIG. 2 that both the AC component and the DC componentare included in the receiving information. The AC component may berelated to a pulsating blood volume. In the case of weakest blood flow,the light absorption intensity of the blood may be smallest, while thetransilluminated signal may be strongest, and thus the AC signal canreach a maximum value. On the other hand, the light absorption intensityof the blood may become largest, while the transilluminated signal maybe weakest, and thus the AC signal may reach a minimum value. The DCcomponent is caused by non-pulsating transilluminated intensity ofmuscle and bone, and it represents a minimum signal value.

In an example SaO₂ sampling system, e.g., in Mindray™'s blood oxygensystem, a voltage range of a sampled signal can be about 0-5 V, and itsmapped sampled data can have a range of about 0-2,097,152 V. As aresult, a calculated least significant bit (LSB) value can be about2.38, namely each sampled value may correspond to 2.38^(μV). In thisdisclosure, various features of respective blood oxygen parameters canbe described according to voltage characteristic.

Basic hemodynamic features may disappear with a cardiac arrest, in whichcase the sampled signal may look like a noise line. When there is ROSC,the sampled signal can have corresponding regular pulse features.Therefore, whether there is ROSC after defibrillation can be recognizedbased on a change from noise line to regular pulse feature. Pulsefeature recognition based on a sampled signal can be non-invasive,convenient, high in response speed and adaptive for the urgent CPRprocess. In this disclosure, a method for recognizing ROSC afterdefibrillation is provided on the basis of sampling red/infrared lightsignals. Since the infrared light is less interfered with when comparedwith the red light, this disclosure describes the above-mentioned ROSCrecognition method after defibrillation using the sampled infrared lightsignals as an example (FIG. 5).

In order to eliminate noise interference beyond physiological band, theinfrared light signals can first be filtered in real time by using aband pass filter of about 0.34-5.0 Hz. A sliding time window can beestablished to find out whether the pulse features appear therein. ROSCafter defibrillation can be determined when there are respectively noiselines and regular pulses wave at the front and back ends of the timewindow, the pulse wave number reaches about 4-6, and a single pulse wavehas relatively good quality. According to current guidelines for CPR, aninterruption period between compressions should not exceed about 10seconds. Therefore, such recognition can be performed precisely whenthere are at least about six pulse waves within about 10 seconds. Aspontaneous cardiac rhythm more than about 36 times per minute can thusbe accurately recognized by the method of this disclosure, where thespontaneous cardiac rhythm (time per minute) can be obtained bymultiplying a frequency of spontaneous cardiac rhythm by 60. In thisembodiment, the pulse wave number used as a determination criterion canbe adaptively set according to system features, and a time duration ofthe sliding time window can be adaptively adjusted according to thefrequency of the pulse wave. Quality of the single pulse wave can bedetermined by its amplitude, width and shape. An amplitude inspectionmay refer to amplitude consistency (e.g., with fluctuation of less thanabout 10%) between a current pulse wave and at least about threehistorical pulse waves. When those pulse waves have no consistency inamplitude, the amplitude quality may be deemed to be poor. A widthinspection may refer to width consistency (e.g., with fluctuation ofless than about 10%) between the time lengths (i.e., cardiac rhythm) ofthe current pulse wave and the at least about three historical pulsewaves. When those pulse waves have no consistency in width, the widthquality may be deemed to be poor. A shape inspection may refer to ashape correlation between the shapes of the current pulse wave and theat least about three historical pulse waves. When the correlation isless than about 80%, the shape quality may be deemed to be poor. Byconsidering amplitude, width and shape, the quality level of the singlepulse wave can be obtained for further evaluation. In this embodiment,the thresholds can be adaptively adjusted according to system features.The recognition of a pulse wave within the sliding time window can beperformed by any suitable recognition method such as a difference methodand an inflection point method.

Based on the physiological features of cardiac motion in the CPRprocess, there may be two change modes for cardiac impulse. On one hand,the cardiac impulse may suddenly appear and become stable immediately.On the other hand, the pulses may grow stronger gradually and becomestable finally. FIGS. 3 and 4 respectively show these two change modes.

In FIG. 3, the “defibrillation” period shows that the sampled signalshave no physiological features of the pulse wave. The “ROSC” periodshows that the patient has restored his/her spontaneous circulation withstable and regular pulse waves after a successful defibrillation.

In FIG. 4, the “defibrillation” period shows that the sampled signalshave no physiological features of the pulse wave. The “ROSC” periodshows that the patient has restored his/her spontaneous circulationafter a successful defibrillation, where the regular pulse waves growstronger and gradually become stable.

When performing external compressions in the presence of spontaneouscirculation, filling and ejection functions of a normal heart may beinterfered due to non-synchronization between rhythm and time phase ofmanual compression and cardiac systolic and diastolic functions underthe spontaneous circulation, which may influence normal cardiac pumpfunction and cause reduction in stroke volume. In an initial stage ofROSC, the patient's heart rate may have gradual changes from slow tofast due to vasoactive drugs used in a rescue process, and stimulationfrom the external compression and physiological compensation mechanism.However, there may not be spontaneous cardiac rhythm with gradualchanges in patients having serious heart failure, abnormal conduction,or abnormal cardiac electrical activity. This may be related to thepathological loss of the compensation mechanism. These patients may haveslow or fast arrhythmia, i.e., their cardiac rates are less than about60 times (beats) per minute or more than about 120 times per minute,which can be distinguished from external compression frequency.

When the patient has restored his/her spontaneous circulation in the CPRprocess, the manual compression may disturb the normal spontaneouscirculation, and the spontaneous cardiac rhythm can have gradual changesif the patient has relatively good cardiac function. On the other hand,the spontaneous cardiac rhythm may be maintained at a rate of less thanabout 60 times per minute or more than about 120 times per minute if thepatient has relatively poor cardiac function. All these can provideadvantageous physiological features for the ROSC recognition in the CPRprocess of this disclosure.

Two kinds of signals can be found when analyzing the features of ROSC inthe CPR process. That is, the manual compression can form one waveformsignal, while the spontaneous circulation can form another waveformsignal. Those signals formed by the manual compression and thespontaneous circulation (i.e., manual compression signal and spontaneouscirculation signal) may be superposed and mixed to form a specialwaveform showing there is ROSC in the CPR process.

The manual compression signal may be formed by stable compression with afixed frequency and fixed depth, such as a sine wave of about 100 BPM.The spontaneous circulation signal may have a fixed frequency, such as asine wave of about 80 BPM. As shown in FIG. 6, the mixed waveform of themanual compression signal and the spontaneous circulation signal mayhave a regular envelope. As described by the envelope line and the areashowing the “effect of mixed ROSC and CPR,” the regular envelopecharacteristic can be identified, which cannot be shown by anysingle-way signal. This envelope characteristic thus can provide aneffective feature point for the ROSC recognition in the CPR process.

In practical application, there should be phase deviation between themanual compression signal and the spontaneous circulation signal, i.e.,these two signals are not in 0° phase alignment with each other. Asshown in FIGS. 7A-7D, there may be phase deviations of 0°, 90°, 180° and270° between the manual compression signal and the spontaneouscirculation signal, which can indicate the envelope specificity of themixed waveform. Based on some theoretical analysis, those fixed phasedeviations may have no apparent influence on the envelope characteristicof the mixed signal. That is, the phase difference may not affect theregularity of an envelope rhythm.

As described by such physiological features, the spontaneous cardiacrhythm can have gradual changes during external compression. Based onthis point, a mode can be deduced in this disclosure, where the manualcompression signal can be a sine wave of fixed 100 BPM, and thespontaneous cardiac rhythm signal can be a sine wave with gradual changefrom about 80 BPM to about 120 BPM. The mixed waveform of these twosignals is shown in FIG. 8.

According to the deduced mode, the mixed waveform may still have aregular envelope rhythm characteristic when the spontaneous cardiacrhythm increases gradually. An envelope shape may depend on thespontaneous cardiac rhythm and the compression frequency. The larger thefrequency deviation, the narrower the envelope width. The smaller thefrequency deviation, the wider the envelope width. Herein, the widestenvelope in FIG. 8 corresponds to the situation where the spontaneouscardiac rhythm and the compression frequency are the same.

As shown in FIGS. 9A-9D, the phase deviation between the spontaneouscardiac rhythm and the compression frequency may have no influence onthe envelope rhythm characteristic.

As described above, with the occurrence of ROSC, the physiologicalsignals can often have the envelope characteristic during externalcompression. Therefore, it can be deemed that the patient has restoredhis/her spontaneous circulation when some continuous and regularenvelope characteristics are recognized. The following exemplary featurerecognition logic can be established based on such features.

Peak points of each pulse wave of the physiological signals can berecognized by any suitable methods such as a difference transformationmethod and a slope transformation method. Missing parts between the peakpoints of the pulse waves can then be compensated by linearinterpolation or curve fitting to maintain time synchronization withsome original sampled signals and form a positive envelope curve (PEcurve).

Time information

$\sum\limits_{n = 0}^{N - 1}\; {{PE}_{Time}(n)}$

and amplitude information

$\sum\limits_{n = 0}^{N - 1}\; {{PE}_{Amp}(n)}$

of the peak points of the PE curve can be recognized and recorded by anysuitable methods such as a difference transformation method and a slopetransformation method.

Valley points of each pulse wave of the physiological signals can berecognized by any suitable methods such as a difference transformationmethod and a slope transformation method. Missing parts between thevalley points of the pulse waves can then be compensated by linearinterpolation or curve fitting to maintain time synchronization withsome original sampled signals and form a negative envelope curve (NEcurve).

Time information

$\sum\limits_{n = 0}^{N - 1}\; {{NE}_{Time}(n)}$

and amplitude information

$\sum\limits_{n = 0}^{N - 1}\; {{NE}_{Amp}(n)}$

of the valley points of the NE curve can be recognized and recorded byany suitable methods such as a difference transformation method and aslope transformation method.

Width information of each envelope can be recognized and recorded.Variations of amplitude

$\sum\limits_{n = j}^{N - 1}\; {{PNE}_{Amp}(n)}$

(i.e., envelope amplitude PNE) of corresponding time points on PE and NEcan be evaluated by any suitable methods such as a differencetransformation method and a slope transformation method. After that, thetime information

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{Time}^{Max}(n)}$

corresponding to a maximum

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{Amp}^{MAX}(n)}$

of the envelope amplitude PNE or the time information

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{Time}^{Min}(n)}$

corresponding to a minimum

$\sum\limits_{n = 0}^{N - 1}\; {PNE}_{Time}^{Min}$

of the envelope amplitude PNE can be found. A time width

$\sum\limits_{n = 0}^{N - 2}\; {{PNE}_{trueWidth}(n)}$

of the envelope can be obtained when calculating a D-value between thetimes at an Nth maximum and an N−1th maximum or at an Nth minimum and anN−1th minimum. Some related formulas are shown as below.

${\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{Amp}(n)}} = {{\sum\limits_{n = 0}^{N - 1}\; {{PE}_{Amp}(n)}} - {\sum\limits_{n = 0}^{N - 1}\; {{NE}_{Amp}(n)}}}$${\sum\limits_{n = 0}^{N - 2}\; {{PNE}_{tmWidth}(n)}} = {\sum\limits_{n = 1}^{N - 1}\; \left\lbrack {{{PNE}_{Time}^{Max}(n)} - {{PNE}_{Time}^{Max}\left( {n - 1} \right)}} \right\rbrack}$${\sum\limits_{n = 0}^{N - 2}\; {{PNE}_{tmWidth}(n)}} = {\sum\limits_{n = 1}^{N - 1}\; \left\lbrack {{{PNE}_{Time}^{Min}(n)} - {{PNE}_{Time}^{Min}\left( {n - 1} \right)}} \right\rbrack}$

Time points

$\sum\limits_{n = 0}^{N - 1}\; {{PE}_{Time}^{Max}(n)}$ and$\sum\limits_{n = 0}^{N - 1}\; {{NE}_{Time}^{Min}(n)}$

that correspond to each envelope maximum

$\sum\limits_{n = 0}^{N - 1}\; {{PE}_{Amp}^{Max}(n)}$

of the PE amplitude and to each envelope minimum

$\sum\limits_{n = 0}^{N - 1}\; {{NE}_{Amp}^{Min}(n)}$

of the NE amplitude can be first determined. After that, an envelopetime deviation factor

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{tmBias}(n)}$

can be established by the determined time points. Its calculationformula is shown below:

${\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{tmBias}(n)}} = {\sum\limits_{n = 1}^{N - 1}\; \left\lbrack {{{PE}_{Time}^{Max}(n)} - {{NE}_{Time}^{Min}(n)}} \right\rbrack}$

Based on the above-described recognition logic, the followingcharacteristic values can be established: maximum

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{Amp}^{Max}(n)}$

and minimum

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{Amp}^{Min}(n)}$

information of envelope amplitude, envelope width information

${\sum\limits_{n = 0}^{N - 2}\; {{PNE}_{tmWidth}(n)}},$

and envelope time deviation factor

$\sum\limits_{n = 0}^{N - 1}\; {{{PNE}_{tmBias}(n)}.}$

Whether there is a regular envelope feature can be evaluated by theseestablished characteristic values. The maximum

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{AMP}^{Max}(n)}$

and minimum

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{AMP}^{Min}(n)}$

information of the envelope amplitude may correspond to an amplituderatio that should meet a certain value. The envelope width information

$\sum\limits_{n = 0}^{N - 2}\; {{PNE}_{tmWidth}(n)}$

and the envelope time deviation factor

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{tmBias}(n)}$

should also meet a certain ratio, and the envelope time deviation factor

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{tmBias}(n)}$

should be smaller than a certain time length. After meeting the criteriaabove, whether there are at least about three continuous envelopes canbe evaluated. ROSC can be determined when at least about threecontinuous envelopes can be detected, and the amplitude

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{Amp}^{Max}(n)}$

as well as the width

$\sum\limits_{n = 0}^{N - 1}\; {{PNE}_{Amp}^{Min}(n)}$

have small fluctuations or meet a certain increasing or decreasingtendency feature. Otherwise, there is no spontaneous cardiac rhythm.FIG. 10 shows a flow chart for such exemplary time-domain recognitionlogic.

Due to the differences between the compression frequency and thespontaneous circulation frequency, the formed envelope may includedifferent numbers of pulse peaks. When evaluating according to theenvelope, the evaluation time may be inconsistent. Generally speaking,ROSC can be determined and recognized within about 30 seconds. Herein,FIG. 11 shows some calculation parameters on a waveform.

As shown in FIG. 12, such regular envelope has been found in animalexperiments. The above-described time-domain feature recognition logiccan precisely find and recognize ROSC in the CPR process while furtherproviding some prompt information.

Time-domain feature recognition is well-known to present dynamic signalson a time axis, thereby having the advantages of accuracy and intuition,and having a relatively high correlation with the shape of the pulsewave. When there is motion interference, baseline drift and patientcardiac dysfunction, the waveform in time domain may have disorder, inwhich case the recognition based on the envelope feature may beaffected. As shown in FIG. 13, the pulse wave has some physiologicalecho (which is caused by ejection rebound of a blood vessel), and theenvelope feature thus shows certain interfered characteristics. Throughparticular mathematic rules, the frequency domain method can convert thesignals into a figure with a frequency axis as the coordinate. In thiscase, the signals with different frequencies can be reflected atdifferent locations on the frequency axis directly. It can be seen thatthe frequency domain method may have some advantages when compared withthe time domain method. As a result, this disclosure further describesthe feature recognition of ROSC in the CPR process in terms offrequency-domain feature recognition.

Phase deviation of a time-domain waveform can be eliminated in thefrequency domain due to frequency-domain characteristics. If thespontaneous cardiac rhythm and the external compression frequency aredifferent, two independent spectral peaks can be formed in the frequencyspectrum. Assuming that the spontaneous cardiac rhythm is a sine wave ofabout 80 BPM and the external compression frequency is a sine wave ofabout 100 BPM, their mixed waveform and spectral distribution can beshown as FIG. 14. Theoretically, two spectral peaks at differentfrequencies would be present in the frequency spectrum when thespontaneous cardiac rhythm and the external compression frequency aredifferent. As shown in FIG. 14, the spontaneous cardiac rhythm can forma spectral peak at about 1.33 Hz, while the external compression canform a spectral peak at about 1.67 Hz.

The physiological signals may often include several sine waves insteadof a single sine wave. In clinical applications, both the externalcompression signals and the spontaneous cardiac rhythm signals can haveharmonic spectral components. Assuming that the spontaneous cardiacrhythm is a sine wave of about 80 BPM (having first harmonicinformation) and the external compression frequency is a sine wave ofabout 100 BPM (having first harmonic information), their mixed waveformand spectral distribution can be shown as FIG. 15. Theoreticallyspeaking, harmonic frequency may often be n multiples of a basefrequency (e.g., n=1, 2, 3 . . . N), namely an nth harmonic spectralpeak can be found on the frequency axis at n multiples of the basefrequency. As shown in FIG. 15, a first harmonic frequency of thespontaneous cardiac rhythm can be found at about 2.67 Hz, while a firstharmonic frequency of the external compression frequency can be found atabout 3.33 Hz. It can be seen that the harmonic components would neithercause interference to the base frequency information nor have mutualinterference among themselves, and the spectral peaks of the externalcompression and the spontaneous cardiac rhythm may still be located atdifferent frequencies.

A formula for a harmonic location is as follows:

F_(Harmonic)(n)=F_(Basic)*(1+n), where F_(Harmonic)(n) represents thefrequency of an nth harmonic component and F_(Basic) represents the basefrequency.

The descriptions above have shown the situation where the externalcompression frequency and the spontaneous cardiac rhythm areinconsistent. In clinical practice, those two frequencies can bebasically consistent with each other. Assuming that both the spontaneouscardiac rhythm and the external compression frequency are sine waves ofabout 100 BPM, their spectrogram can be shown as FIG. 16. For thepurpose of contrasting with a mixed spectral peak (a higher spectralpeak) formed by the spontaneous cardiac rhythm and the externalcompression frequency of about 100 BPM, two lower spectral peaks in thisfigure respectively stand for the spontaneous cardiac rhythm of about 80BPM and the external compression frequency of about 100 BPM. Amplitudeof an overlapping spectral peak is a summation of the amplitudes of therespective independent spectral peaks. As shown in FIG. 16, theamplitude of the mixed spectral peak is a summation of the spectralpeaks formed by the spontaneous cardiac rhythm and the externalcompression. It can be seen that the spectral amplitude can havesignificant changes in the case of the overlapping frequency.

The physiological signals may often include several sine waves insteadof a single sine wave. Assuming that the spontaneous cardiac rhythm is asine wave of about 100 BPM (having first harmonic information) and theexternal compression frequency is a sine wave of about 100 BPM (havingfirst harmonic information), their spectral distributions can be shownas FIG. 17. Theoretically speaking, the harmonic component would have noinfluence on the accumulation effect of the overlapping frequencies. Asshown in FIG. 17, the overlapping spectral peak at the base frequencystill has an amplitude as a summation of the amplitudes at the basefrequencies of the spontaneous cardiac rhythm and the externalcompression frequency, and the overlapping first harmonic spectral peakstill has an amplitude as a summation of the amplitude of the firstharmonic spectral peaks of the spontaneous cardiac rhythm and theexternal compression frequency. Lower spectral peaks in FIG. 17respectively stand for the base frequency information and the harmonicfrequency information of the spontaneous cardiac rhythm of about 80 BPMand the external compression frequency of about 100 BPM. Those lowerspectral peaks may function as the amplitude reference so as to show theamplitude accumulation effect.

As described above, the physiological signals (detected by a pulseoximeter) may exhibit apparent spectral features with the presence ofspontaneous cardiac rhythm during external compression. In the casewhere there are inconsistent frequencies between the spontaneous cardiacrhythm and the external compression, two spectral peaks can be formed inthe frequency spectrum; in contrast, the amplitude of the spectral peakmay have significant change. For these reasons, the patient may havespontaneous circulation function when there are spectral peaks atdifferent frequencies, or when the amplitudes of certain spectral peakshave significant changes. Based on such features, an exemplaryfrequency-domain recognition logic can be established as below.

Sampled infrared light signals can be selected as original signals forthe spectral analysis. Sampled red light signals or both kinds ofsignals can also be used for the analysis. In this disclosure, since theinfrared light may have better anti-inference capability, thecorresponding analysis will be carried out based on the infrared light.The sampled infrared light signals within a certain time duration can beselected as the data to be converted for the spectral analysis. In thecase where the data segment is too long, there may be a time delay forthe feedback of physiological information. In the case where the datasegment is too short, the physiological information may be insufficient.Such time duration can be adaptively adjusted according to therequirements of system analysis. In combination with practicalapplications, the data within about four to six seconds may be chosen tobe converted for the spectral analysis.

The time-domain data within about four to six seconds may be convertedinto corresponding frequency-domain data. Fast Fourier Transformation(FFT) or Chirp z-transform (CZT) can be used for this conversion. Forexample, CZT has been used in this disclosure. In order to improvesignal-to-noise ratio (SNR), partial noise elimination, such as baselinedrift elimination, high-frequency noise elimination and catastrophepoint suppression may be performed on the time-domain data before thetime-frequency conversion. The filter method and/or root mean squaremethod can be used in this disclosure for eliminating the baselinedrift, the high-frequency noise and the catastrophe point. Some othermethods, such as the wavelet transform method, neural network method andadaptive comb filter method, can also be used for removing the noiseinterference. In order to enhance the main spectral peak and reducesidelobe interference, a window can be increased for sidelobesuppression. For instance, a Kaiser window may be used for sidelobesuppression in this disclosure.

Each spectral peak information of the frequency-domain data, i.e.,frequency location

$\sum\limits_{n = 0}^{N - 1}\; {{fPeak}_{Freq}(n)}$

and amplitude value

${\sum\limits_{n = 0}^{N - 1}\; {{fPeak}_{Amp}(n)}},$

may be searched and recorded. The difference transformation method andslope transformation method may be used to recognize the spectral peakinformation. The search may be carried out as follows: when therecognized spectral peak has a frequency beyond a physiological range ofabout 0.3-5 Hz, or when the recognized spectral peak has an amplitudeless than that of a maximum spectral peak by about 4%, the correspondingspectral peak will be discarded.

The frequency location

$\sum\limits_{n = 0}^{N - 1}\; {{fPeak}_{Freq}(n)}$

and the amplitude value

$\sum\limits_{n = 0}^{N - 1}\; {{fPeak}_{Amp}(n)}$

may be searched once again so as to obtain the spectral peaks of theexternal compression frequency and the spontaneous cardiac rhythm, andthey may be re-recorded as the frequency location

$\sum\limits_{n = 0}^{N - 1}\; {{fPeak}_{Freq}^{P}(n)}$

and the amplitude value

$\sum\limits_{n = 0}^{N - 1}\; {{fPeak}_{Amp}^{P}(n)}$

(p means probable peak information). The search may be carried out asfollows: when a certain spectral peak has a harmonic component, acorresponding harmonic spectral peak is cancelled. The sidelobe peak ofeach spectral peak may be searched and deleted according to a spectraltheoretical formula.

The external compression frequency should be maintained at about 110 BPM(1.83 Hz) according to CPR guidelines. Therefore, the spectral peakinformation of the external compression frequency, i.e., frequencylocation fPeak_(Freq) ^(CPR) and amplitude value fPeak_(Amp) ^(CPR), canbe determined by searching the frequency location

$\sum\limits_{n = 0}^{N - 1}\; {{fPeak}_{Freq}^{P}(n)}$

and the amplitude value

$\sum\limits_{n = 0}^{N - 1}\; {{{fPeak}_{Amp}^{P}(n)}.}$

The determination rule can be as follows: those spectral peaks of about110 BPM±10 BPM (1.83 Hz±0.17 Hz) can be deemed as the spectral peak ofthe external compression frequency.

A sliding time window can be established to evaluate the changes of thefrequency location fPeak_(Freq) ^(CPR) and amplitude value fPeak_(Amp)^(CPR). It can be deemed that the spontaneous cardiac rhythm and theexternal compression frequency are basically consistent in the followingsituations: the frequency is within the sliding time window and stableat about 110 BPM±10 BPM (1.83 Hz±0.17 Hz), while the amplitude valuefPeak_(Amp) ^(CPR) has significant changes (e.g., at least about 20%fluctuations). The time duration of the sliding time window can be setaccording to system operation. For example, an observation time can beset to be about six to eight seconds. As shown in FIG. 18, provided thatboth the spontaneous cardiac rhythm and the external compressionfrequency are about 110 BPM (1.83 Hz), the spontaneous cardiac rhythmmay occur at about the sixth second with increasing amplitude and becomestable in amplitude at about the 11th second. Besides, overlappingamplitude may appear at the spectral peak due to the same frequency.FIG. 18 shows that the amplitude of the mixed frequency spectrumresulted from the spontaneous cardiac rhythm, and the compressionfrequency can gradually increase and finally become stable. Through theabove-described sliding time window, the spontaneous cardiac rhythm canbe determined by evaluating the variation characteristic of the spectralamplitude within the sliding time window.

The frequency location fPeak_(Freq) ^(CPR) and the amplitude valuefPeak_(Amp) ^(CPR) can be evaluated comprehensively, so that thespectral peaks (except that of the external compression) may be used asthe evaluation subject. A sliding time window can be established toobserve a distribution range of the frequency spectrum variationstherein in every second. Within the observation period, the frequencyspectrum and the amplitude of the spontaneous cardiac rhythm can befound in the frequency spectrum continuously, while other interferingspectral peaks may not show any continuous characteristics because ofrandom distribution of white noise. Therefore, whether there isspontaneous cardiac rhythm can be determined according to suchcontinuously stable characteristics of the spectral peak over time. Thetime duration of the sliding time window can be adaptively adjustedaccording to system features. For instance, when combining with thefeatures of the pulse oximetry system described in this disclosure, thesliding time window may be set to about six to eight seconds. It can bedetermined that the spontaneous cardiac rhythm has been restored and itsfrequency is inconsistent with that of the external compression in thefollowing situations: a certain spectral peak lasting for about six toeight seconds is detected, and its amplitude in the frequency spectrumper second is larger than a certain proportion of a maximum amplitude ofthe spectral peak of the external compression frequency. This proportioncan be adjusted based on system features. A lower proportion can lead tohigh sensitivity but also increased risk of misrecognition. In thisdisclosure, a default proportion can be about 20%. As shown in FIG. 19,assuming that the spontaneous cardiac rhythm can be about 80 BPM and theexternal compression frequency can be about 110 BPM, the spontaneouscardiac rhythm can be recognized based on a continuous evaluation of thesliding time window.

Physiological specificities have noted that the frequency of thespontaneous circulation can show a gradually increasing tendency in thecompression process. For this reason, after detecting a spectral peaklasting for about six to eight seconds and having an amplitude of acertain proportion (e.g., about 20% in this disclosure) of that of theexternal compression frequency, a monotonicity evaluation should furtherbe carried out on the variation tendency of the location of the spectralpeak. It can be deemed that the spontaneous cardiac rhythm has beenrestored when all the situations above are met. Assuming that theexternal compression frequency is about 120 BPM (2.0 Hz), and a pulserate of the spontaneous cardiac rhythm increases gradually from about 70BPM to about 100 BPM (1.167 Hz-1.667 Hz) over time, the graduallyincreasing effect of the spontaneous cardiac rhythm in the compressionprocess can be found in FIG. 20.

As the spontaneous cardiac rhythm increases, it may become overlappedwith the external compression frequency. That is, the spontaneouscardiac rhythm and the external compression frequency may be the same ata time instant. In this case, the above-described feature recognitionshould be combined in the recognition process. On one hand, whetherthere is a continuous spectral peak in about six to eight seconds,whether its amplitude is at least a certain proportion to that of theexternal compression frequency, and whether this spectral peak has amonotone increasing tendency in the frequency should be evaluated. Onthe other hand, the amplitude variation should be evaluated for theexternal compression frequency. After all the situations above are met,ROSC can be determined. Assuming that the spontaneous cardiac rhythmchanges from about 100 BPM to about 140 BPM (1.67 Hz-2.33 Hz) and theexternal compression frequency maintains at about 120 BPM (2 Hz), FIG.21 shows how the spontaneous cardiac rhythm increases gradually andoverlaps with the external compression frequency, where the overlappingamplitude can be found in the case when the two frequencies areconsistent.

For restoration of spontaneous circulation during external compression,the descriptions above have illustrated how to recognize the spontaneouscardiac rhythm from time domain and frequency domain respectively. Thetime-domain logic can recognize the spontaneous cardiac rhythm duringexternal compression from the shape feature of the mixed waveform thatresulted from waveform superposition. The frequency-domain logic canrecognize the spontaneous cardiac rhythm during external compression byevaluating whether the spontaneous cardiac rhythm and the externalcompression frequency can overlap with each other. The time-domainrecognition may mainly depend on the shape feature, which can benon-obvious in the case of low SNR caused by motion interference or lowhemoperfusion. The frequency-domain recognition may mainly depend on thefrequency and amplitude information evaluation based on the frequencyspectrum, thus exhibiting good anti-interference capability. Therefore,for the purpose of improving reliability and stability, ROSC may beevaluated comprehensively during external compression based on thefrequency-domain analysis while combining with the time-domain analysis.Alternatively, the frequency-domain or the time-domain analysis can beused independently for ROSC recognition during external compression.

Through a large number of external compression data in animalexperiments, the above-described methods can realize the ROSCrecognition during external compression very well. At this point, thefollowing feature points may further be defined in combination with thevariations of the clinical physiological features.

Provided that the spontaneous cardiac rhythm and the externalcompression frequency are inconsistent with each other, the patient mayhave good ROSC when the amplitude of the spectral peak of thespontaneous cardiac rhythm is larger than or equal to the spectral peakamplitude of the external compression. For this reason, fRatio can beestablished:

${{{fRatio}(n)} = \frac{{fPeak}_{Amp}^{ROSC}(n)}{{fPeak}_{Amp}^{CPR}(n)}},$

where fRatio represents a ratio at an nth second between the amplitudesof the spectral peaks of the spontaneous cardiac rhythm and the CPR.

A probability when the fRatio is continuously larger than or equal to1.0 is counted in a sliding time window of about six to eight seconds.When the probability is at least about 75% (which can be adaptivelyadjusted according to system features), the ROSC and the restorationcondition can be determined. In this way, some technical prompt and/orwarning information can be provided based on the determination results.

Provided that the spontaneous cardiac rhythm and the externalcompression frequency are basically consistent with each other, theamplitude of the spectral peak resulting from peak overlapping maydecrease. For this reason, both the increasing and the decreasingtendencies of the amplitude of the spectral peak should be evaluated inthe event that spontaneous cardiac rhythm and external compressionfrequency are consistent. In the sliding time window of about six toeight seconds, the ROSC can be determined when the amplitude of thespectral peak of the external compression decreases continuously byabout 25%-60% (when compared with the amplitude at an initial moment ofthe sliding time window) and its corresponding decreasing durationaccounts for at least about 70% (which can be adaptively adjustedaccording to system features) of the sliding time window. In this way,some technical prompt and/or warning information can be provided basedon the determination results to avoid any cardiac damage.

Although the spontaneous cardiac rhythm can be restored through the CPRemergency treatment, the spontaneous circulation may be unstable and thecardiac arrest may happen again because of ischemia, hypoxia andreperfusion injury of the heart and microcirculation in the CPR process.Once failing to detect such unstable condition or cardiac arrest, anoptimal rescue time may be delayed. Therefore, in order to find and warnof any unstable spontaneous circulation for timely treatment, anevaluation and feedback system for circulation quality should beestablished after ROSC is determined. In this disclosure, a practicalmechanism for quality evaluation of the spontaneous cardiac rhythm canbe established according to the features of the spontaneous cardiacrhythm. Post-ROSC circulation quality evaluation which may also becalled ROSC quality for short will be described in detail below.

For system sampling signals, the pulse waves formed by the externalcompression and the spontaneous circulation are basically consistentwith each other, where their difference may be whether the cardiacmotion is caused by manual compression or spontaneous circulation. Basedon this point, a series of parameters for evaluating compression qualitydisclosed in a patent application CN201310474008.7 can also be used forthe quality evaluation of the spontaneous circulation. This disclosuredescribes how to evaluate the quality of restoration of the spontaneouscardiac rhythm using PVPI parameters disclosed in the patent applicationCN201310474008.7 as an example.

Original signals I can include AC components S_(AC) and DC componentsS_(DC). In some cases, certain factors such as body movement andbackground light interference may result in a drift of the DC componentsS_(DC) over time, namely its numerical value may not be constant but canfluctuate with time. By using suitable technologies such as averagevalue technology, smooth filtering technology, finite impulse responsedigital filter/infinite impulse response digital filter (FIR/IIR)filtering technology or curve fitting technology, the DC componentsS_(DC) can be filtered out of the original signals I and the ACcomponents S_(AC) can be left for further data processing. FIG. 22 showsthe AC components S_(AC) after filtering out the DC components S_(DC).

The AC components S_(AC) may be related to blood flow, and its frequencycan be consistent with the CPR compression frequency. The frequency ofthe AC components S_(AC) can be multiplied by 60, so as to obtain theblood oxygen frequency, namely CPR compression times per minute.

F _(ROSC) =F _(S) _(AC)

where F_(ROSC) represents the CPR compression frequency, F_(S) _(AC)represents the frequency of the AC components S_(AC), and the unit ofboth is Hertz (Hz).

Deg_(ROSC) =F _(ROSC)*60=F _(S) _(AC) *60

where Deg_(ROSC) represents the CPR compression times per minute, andits unit is time per minute.

By combining Deg_(ROSC) and ECG, electrical mechanical dissociation orsevere peripheral circulatory failure can be detected on the patient.

It is known from Parseval theorem that there can be energy conservationbetween time domain and frequency domain, and thus those parametersdescribed below can be calculated by two methods, i.e., the time-domainmethod and the frequency-domain method.

Area characteristics of each pulse wave of the AC components S_(AC) canbe calculated to evaluate the variations of the stroke volume of thespontaneous cardiac rhythm so that the quality of restoration of thespontaneous cardiac rhythm can be reflected indirectly. The area valueof each pulse wave can be calculated by any suitable technologies suchas the area integral method, which may be applicable to both continuoussignals and discrete signals. In this embodiment, based on the featuresof fixed sampling frequency of blood oxygen technology, the method ofpoint-by-point accumulation integral can be used to calculate the areaparameter.

Formula in time domain:

${Area}_{ROSC} = {\sum\limits_{n = 0}^{N - 1}\; {S_{AC}(n)}}$

where S_(AC)(n) represents the nth sampling data point of each pulsewave, n represents the current data point of the single pulse wave, andN represents the total data length of the single pulse wave. Area_(ROSC)can be a stroke volume parameter, and its unit can be defined as PVPG(Pulse Oximeter Voltage Plethysmography), which is also called voltagevolume.

Formula in frequency domain:

${Area}_{ROSC}^{*} = {\sum\limits_{n = 0}^{N - 1}\; \left( {\sum\limits_{k = 0}^{K - 1}\; {X_{fn}(k)}} \right)}$

where Area*_(ROSC) can be a stroke volume parameter, and its unit can bedefined as PVPG (Pulse Oximeter Voltage Plethysmography), which is alsocalled voltage volume, n represents the current effective frequencycomponent f_(n), N represents the total number of the effectivefrequency components, k represents the data point of the currenteffective frequency f_(n), and K represents the total data length of theeffective frequency component f_(n).

The parameters Area_(ROSC) and Area*_(ROSC) can reflect the strokevolume indirectly, which means they are not directly equal to the strokevolume. In theory, they should exhibit a linear positive correlationwith the cardiac ejection volume in every compression.

According to the characteristics of the blood oxygen system, drivecurrent regulation should be performed depending on the signalconditions, so as to make the sampled blood oxygen signals fall within ameasurable range. As shown in FIG. 23, the variations of the drivecurrent can lead to the changes of the AC components and the DCcomponents by the same rate. In FIG. 23, the signal in the solid linefalls within a lower measurement range. In this case, drive regulationcan be made so that the signals would fall within a reasonablemeasurement range. For example, after a double drive regulation, thesignal in dotted line as shown in FIG. 23 locates at a middle positionof the measurement range. According to the drive characteristic, it isknown that AC2=AC1*2 and DC2=DC1*2.

As a result, the above-described PVPG parameters, i.e., Area_(ROSC) andArea*_(ROSC), can further be transformed as follows when referring tothe changes of the corresponding DC components of the pulse wave:

Formula in time domain:

${AreaIndex}_{ROSC} = {\frac{{Area}_{ROSC}}{\left( {\sum\limits_{n = 0}^{N - 1}\; {S_{DC}(n)}} \right)/N} = \frac{\sum\limits_{n = 0}^{N - 1}\; {S_{AC}(n)}}{\left( {\sum\limits_{n = 0}^{N - 1}\; {S_{DC}(n)}} \right)/N}}$

where AreaIndex_(ROSC) refers to a CPR quantization index which can bedefined as the voltage volume index, and its unit can be PVPI (PulseOximeter Voltage Amplitude Index).

Formula in frequency domain:

${AreaIndex}_{ROSC}^{*} = {\frac{{Area}_{ROSC}^{*}}{\left( {\sum\limits_{n = 0}^{N - 1}\; {S_{DC}(n)}} \right)/N} = \frac{\sum\limits_{n = 0}^{N - 1}\; \left( {\sum\limits_{k = 0}^{K - 1}\; {X_{fn}(k)}} \right)}{\left( {\sum\limits_{n = 0}^{N - 1}\; {S_{DC}(n)}} \right)/N}}$

where AreaIndex*_(ROSC) refers to a CPR quantization index which can bedefined as the voltage volume index, and its unit can be PVPI (PulseOximeter Voltage Amplitude Index).

The parameters AreaIndex_(ROSC) and AreaIndex*_(ROSC) can be ratiosbetween the area value and its corresponding DC components of the singlepulse wave, thus being capable of reducing the individual difference,removing the interference from drive regulation and having soundanti-interference capacity.

The parameters AreaIndex_(ROSC) and AreaIndex*_(ROSC) can reflect thevariation of the cardiac stroke volume. It is known from thephysiological features that the stroke volume may have a minimum output.The body's peripheral blood circulation is poor or even fails to meetnormal physiological needs when the stroke volume is lower than itsminimum output. Based on such physiological features, there may beminimum area index thresholds for the parameters AreaIndex_(ROSC) andAreaIndex*_(ROSC). If these parameters are lower than those thresholds,the body may exhibit poor spontaneous cardiopulmonary quality and failto meet the normal physiological needs. In case the stroke volume isunstable, or it gradually reduces and even stops over time, the indexesAreaIndex_(ROSC) and AreaIndex*_(ROSC) may have large fluctuations intheir parameter values or they may decrease and even disappear overtime. Based on these features, the post-ROSC circulation quality can berecognized and evaluated effectively.

In order to evaluate the post-ROSC circulation quality, a sliding timewindow may be established so that the time-varying fluctuatingcharacteristics of the parameters AreaIndex_(ROSC) and AreaIndex*_(ROSC)can be evaluated in combination with a lowest physiological threshold.The time duration of the sliding time window can be adaptively adjustedaccording to system features. For example, it can be set as about fourto six seconds. The spontaneous circulation under the spontaneouscardiac rhythm may be unstable when the parameters for spontaneouscardiac rhythm AreaIndex_(ROSC) and AreaIndex*_(ROSC) have significantfluctuations or decrease gradually. Specifically, the unstablespontaneous circulation under the spontaneous cardiac rhythm can bedetermined in the following situation: the parameters in the slidingtime window may decrease by about 20% relative to an average value in 30seconds in an initial stage of the spontaneous cardiac rhythm, where thevalue of the parameters can be recognized on average value and standarddeviation, and the fluctuating proportion can be adaptively adjustedaccording to system requirements; or the parameters may exhibit atime-dependent decreasing tendency and have a maximum decrease by about30% (which can be adaptively adjusted according to system requirements).At this point, some prompt and/or warning information can be provided sothat a doctor can be guided to make an immediate decision to avoidfurther damage.

FIG. 24 is a schematic diagram showing the quality evaluation of thespontaneous cardiac rhythm by using the parameter AreaIndex*_(ROSC) asan example. The “unstable stage” shows that the parameters may befluctuating, where some prompt and/or warning information can beprovided when the fluctuation is larger than about 20%. The “stablestage” shows that the parameters may be relatively stable, and promptinformation can be provided to show sound cardiac rhythm of thespontaneous circulation. The “decreasing stage” shows that theparameters may be decreasing, where some prompt and/or warninginformation can be provided when it has decreased by at least about 20%.Specifically, another prompt and/or warning information can be providedwhen the parameters are lower than the threshold line for a certaintime. The “Sliding window” in FIG. 24 can stand for the sliding timewindow which can slide over time. The fluctuating characteristics of theparameters can be evaluated in the time window, so that the qualityevaluation of the spontaneous cardiac rhythm can be analyzed, and somecorresponding prompt and/or warning information can be provided.

Another index for the quality of the spontaneous circulation is pulserate. The pulse rate can indicate the true tissue perfusion state, whilethe heart rate shown by ECG activity can represent the cardiac electricactivity rhythm. Under the condition of sound spontaneous circulation,the pulse rate should be basically consistent with the heart rate,thereby avoiding severe fluctuations and falling within a reasonablerange. Once the pulse rate decreases continuously by more than about 5%per second relative to the heart rate, becomes lower than about 60 timesper minute, or seems much smaller when compared to the heart rate, thespontaneous circulation may be in an unstable state. There may beelectrical mechanical dissociation or severe peripheral circulationfailure in the case when ECG heart rate is detected without pulse rate.In this case, the spontaneous circulation under the spontaneous cardiacrhythm can be deemed to be unstable, which needs to be dealt withimmediately. Also, some prompt and/or warning information may beprovided so that the doctor can be guided timely to make a decision toavoid further damage.

As shown in FIG. 25, “ECG HR” is a trend line for the ECG heart rate,and “pulse BPM” is a trend line for the ROSC pulse rate. A sliding timewindow can be established to evaluate the differences between the ECGheart rate and the ROSC pulse rate within this time window. There may beprompt/warning information when the ROSC pulse rate is detected todecrease by about 5% (which is a ratio between the ROSC pulse rate andthe ECG heart rate), or when the ROSC pulse rate is detected to be lowerthan about 60 times per minute. In FIG. 25, the “stable stage” indicatesconsistent ECG heart rate and ROSC pulse rate, the “decreasing-1 stage”shows the dissociation of ROSC pulse rate and ECG heart rate in which anelectrical mechanical dissociation may appear, and the “decreasing-2stage” shows that the ROSC pulse rate is continuously lower than about60 times per minute. During the sliding process of the sliding timewindow, the variations of the ECG heart rate and the ROSC pulse rate canbe evaluated therein. Once the “decreasing-1 stage” or “decreasing-2stage” is detected, a warning/prompting message, such as the two arrowspointing to positive and negative directions of the Y axis in FIG. 25,can be provided.

Based on the descriptions above, ROSC recognition devices for real-timerecognition of ROSC in a CPR process, systems for recognition andquality evaluation of ROSC, ROSC feedback systems in a CPR process, andROSC recognition devices without external compression afterdefibrillation can be provided.

FIG. 26 is a block diagram for an ROSC recognition device 100 forreal-time recognition of ROSC in a CPR process according to anembodiment of this disclosure. The ROSC recognition device 100 mayinclude a signal acquisition apparatus 102 and a signal analysisapparatus 104. The signal acquisition apparatus 102 can be used toacquire pulse oximetry waveform signals of a patient. The signalanalysis apparatus 104 can be used to analyze these signals to determinewhether there is ROSC in the CPR process.

In one embodiment, the signal acquisition apparatus 102 can include areceiving tube and a light-emitting tube as shown in FIG. 1. In thisway, it can convert detected red light and/or infrared light penetratingthrough finger arteries into electrical signals so as to obtain thepulse oximetry waveform signals of the patient.

In one embodiment, the signal analysis apparatus 104 can include theabove-described exemplary time-domain recognition logic and/orfrequency-domain recognition logic. The time-domain recognition logiccan be used to recognize whether there is ROSC in the CPR process bydetecting the time-domain envelope of the sampled signals. Thefrequency-domain recognition logic can be used to recognize whetherthere is ROSC in the CPR process by detecting the time-varying featuresof the spectral peak of the sampled signals. For the time-domainrecognition logic, ROSC can be determined when the continuous envelopefeature is recognized; while for the frequency-domain recognition logic,ROSC can be determined when spectral peaks are recognized continuouslyat different frequencies or significant change in the amplitude of thespectral peak is recognized within a certain time.

In one embodiment, the signal analysis apparatus 104 can include twoparts: an algorithm software program and a hardware environment such asa programmable logic device for the running of the algorithm softwareprogram. The programmable logic device may be a flash memory or a RAM.Other suitable programmable logic devices such as the Cortex series canalso be used as a carrier for the algorithm software program.

In one embodiment, the ROSC recognition device 100 can be a functionmodule which may be integrated with any other auxiliary diagnosticequipment (e.g., monitoring device, defibrillator, automatic externaldefibrillator (AED), automatic resuscitator equipment andelectrocardiograph) as a plug-in unit. Alternatively, the ROSCrecognition device 100 can be a one-parameter medical equipment for therecognition of the spontaneous cardiac rhythm during the externalcompression. For instance, the one-parameter medical equipment can havethe shape factor shown in FIG. 27.

FIG. 28 is a schematic diagram for exemplary hardware of the ROSCrecognition device shown in FIG. 26. Specifically, the ROSC recognitiondevice may include a main control panel, an ROSC recognition sub-board,a display (e.g., an LCD screen), a loudspeaker, a battery, a charger andan indicator lamp. Herein, the main control panel may include two parts:a system control part and a power control part. The ROSC recognitionsub-board may function as a source of all the system parameters. It canacquire physiological signals, perform calculations on the physiologicalsignals, output some related parameters and have a communicationinterface with a control system.

FIG. 29 is a schematic circuit diagram for the above-described ROSCrecognition sub-board. The ROSC recognition sub-board may include amicro-programmed Control Unit (MCU) chip, a watchdog circuit, acommunication interface, an I/V conversion circuit, a gain controlcircuit, a bias zoom circuit, a sensor drive circuit and sensors. TheMCU chip can include ROSC recognition algorithms and peripheral controllogics.

FIG. 30 is a block diagram for an ROSC recognition and post-ROSCcirculation quality evaluation system 200 according to an embodiment ofthis disclosure. This system 200 may include the ROSC recognition device100 shown in FIG. 26 and a post-ROSC circulation quality evaluationapparatus 202 for quality evaluation of ROSC.

In one embodiment, the post-ROSC circulation quality evaluationapparatus 202 may be used to evaluate the post-ROSC circulation qualitybased on the variations of the cardiac stroke volume within a certainperiod. After ROSC is determined by the ROSC recognition device, thepost-ROSC circulation quality evaluation apparatus 202 may furthercalculate an area characteristic of AC components of pulse signalssampled by the ROSC recognition device, so as to evaluate the changes ofthe stroke volume under the spontaneous cardiac rhythm and thus giveindirect indication on the quality of restoration of spontaneous cardiacrhythm. Alternatively, the post-ROSC circulation quality evaluationapparatus 202 may evaluate the post-ROSC circulation quality based onthe variations of pulse rate as described above.

In one embodiment, the ROSC recognition and post-ROSC circulationquality evaluation system 200 can be a function module which may beintegrated with any other auxiliary diagnostic equipment (e.g.,monitoring device, defibrillator, AED, automatic resuscitator equipmentand electrocardiograph) as a plug-in unit. Alternatively, the ROSCrecognition and post-ROSC circulation quality evaluation system 200 canbe one-parameter medical equipment for the recognition of thespontaneous cardiac rhythm during the external compression. Forinstance, the one-parameter medical equipment can also have the shapefactor shown in FIG. 27.

FIG. 31 is a schematic diagram for exemplary hardware of the ROSCrecognition and post-ROSC circulation quality evaluation system shown inFIG. 30. Specifically, the ROSC recognition and post-ROSC circulationquality evaluation system may include a main control panel, an ROSCrecognition and post-ROSC circulation quality evaluation sub-board, adisplay (e.g., an LCD screen), a loudspeaker, a battery, a charger andan indicator lamp. Herein, the main control panel may include two parts:a system control part and a power control part. The ROSC recognition andpost-ROSC circulation quality evaluation sub-board may function as asource of all the system parameters. It can acquire physiologicalsignals, perform calculations on the physiological signals, output somerelated parameters and have a communication interface with a controlsystem. The ROSC recognition and post-ROSC circulation qualityevaluation sub-board in this embodiment may have the same function asthe ROSC recognition sub-board shown in FIG. 31.

FIG. 32 is a schematic diagram for an ROSC feedback system 300 in a CPRprocess according to an embodiment of this disclosure. This system 300may include an ROSC recognition and post-ROSC circulation qualityevaluation apparatus 302 and a CPR apparatus 304 respectively connectedto a patient's body. For example, the CPR apparatus 304 can be connectedto a patient's chest, and the ROSC recognition and post-ROSC circulationquality evaluation apparatus 302 can be connected to a patient's finger,forehead or other parts suitable for carrying a probe. The ROSCrecognition and post-ROSC circulation quality evaluation apparatus 302can be used to find out whether a patient has ROSC in the CPR process,and to evaluate the post-ROSC circulation quality thereafter. In thisprocess, the CPR apparatus 304 can be used to provide compression outputon the patient. The ROSC recognition and post-ROSC circulation qualityevaluation apparatus 302 and the CPR apparatus 304 can operate on thehuman body through data interaction to realize automatic compressionrescue and the ROSC recognition and post-ROSC circulation qualityevaluation during the compression process. Specifically, the ROSCrecognition and post-ROSC circulation quality evaluation apparatus 302can control the CPR apparatus 304 to stop its compression output andthen start the post-ROSC circulation quality evaluation when ROSC isdetermined. Subsequently, when the post-ROSC circulation quality isevaluated to be unstable, the ROSC recognition and post-ROSC circulationquality evaluation apparatus 302 may control (or signal) the CPRapparatus 304 to re-start its compression output, and it may perform theROSC recognition once again. FIG. 33 gives detailed internal interactionduring the operation of the system shown in FIG. 32.

The ROSC recognition and post-ROSC circulation quality evaluationapparatus 302 may follow and lock the compression frequency according tothe frequency settings of the CPR apparatus 304. Meanwhile, it candetect the time-domain envelope and/or the time-varying features of thespectral peak in the frequency spectrum in real time. In one embodiment,the ROSC recognition and post-ROSC circulation quality evaluationapparatus 302 can include the exemplary time-domain recognition logicand/or the exemplary frequency-domain recognition logic described above.The time-domain recognition logic can be used to detect the timeenvelopes of the sampled signals in the time domain so as to recognizewhether there is ROSC in the CPR process, while the frequency-domainrecognition logic can be used to detect the time-varying features of thespectral peak of the sampled signals in the frequency domain so as torecognize whether there is ROSC in the CPR process. The time-domainrecognition logic may determine that there is ROSC when continuous andregular envelope features are recognized. The frequency-domainrecognition logic can determine that there is ROSC when spectral peaksare recognized continuously at different frequencies or significantchange in the amplitude of the spectral peak is recognized within acertain time.

The system 300 may control the CPR apparatus 304 to output an acceptablecompression frequency and compression depth for the correspondingpatient according to the parameter information provided by the ROSCrecognition and post-ROSC circulation quality evaluation apparatus 302,thereby improving the patient's survival chance. Once the ROSCrecognition and post-ROSC circulation quality evaluation apparatus 302finds stable ROSC, the whole system may be stopped to avoid any damageto the patient's cardiac functions.

According to another embodiment of this disclosure, an ROSC feedbacksystem in a CPR process 400 can be provided. In addition to an ROSCrecognition and post-ROSC circulation quality evaluation apparatus 402and a CPR apparatus 404 like those in the system 300, this system 400may also include a CPR quality evaluation apparatus 406. In operation,the ROSC recognition and post-ROSC circulation quality evaluationapparatus 402 can control the CPR apparatus 404 to stop its compressionoutput and then start the post-ROSC circulation quality evaluation whenROSC is determined. Subsequently, when the post-ROSC circulation qualityis evaluated to be unstable, the ROSC recognition and post-ROSCcirculation quality evaluation apparatus 402 may control the CPRapparatus 404 to re-start its compression output, and it may perform theROSC recognition once again. During the operation of the CPR apparatus404, the CPR quality evaluation apparatus 406 can interact with the CPRapparatus 404 to recognize the CPR compression condition and providefeedback to control the CPR apparatus 404 to have an effectivecompression output. FIG. 34 shows the detailed internal interactionwhile the system 400 operates.

FIG. 35 is a block diagram of an ROSC recognition device withoutexternal compression after defibrillation 500 according to an embodimentof this disclosure. The device 500 may include a signal acquisitionapparatus 502 for acquiring a patient's pulse wave signals and a signalanalysis apparatus 504 for performing real-time analysis on thesesignals to determine whether there is ROSC after the defibrillation.

In one embodiment, the signal acquisition apparatus 502 can acquire thepatient's pulse wave signals through red light and/or infrared light.For example, the apparatus 502 may include a receiving tube and alight-emitting tube as shown in FIG. 1. In this way, the apparatus 502can convert detected red light and/or infrared light penetrating throughfinger arteries into electrical signals so as to obtain the pulse wavesignals of the patient.

In one embodiment, the signal analysis apparatus 504 can be used todetermine whether there is ROSC after defibrillation as shown in FIG. 5.In one embodiment, in order to eliminate noise interference beyond thephysiological frequency band, the signal analysis apparatus 504 can beused to perform real-time filtering on the signals by a band pass filterbefore analysis. In one embodiment, the signal analysis apparatus 504can establish a sliding time window for the sampled signals, and thendetermine whether there is a pulse feature within the sliding timewindow. Besides, the signal analysis apparatus 504 can determine thatthere is ROSC after defibrillation, if the number of the pulse waves inthe sliding time window exceeds a threshold number, and/or the qualityof a single pulse wave exceeds a threshold quality. The quality of thesingle pulse wave can be determined by its amplitude, width and shape,and the time duration of the sliding time window can be adaptivelyadjusted according to the frequency of the pulse wave.

Those skilled in the art can understand that all or partial steps ofvarious methods in the embodiments can be completed by using a programto command relevant hardware products. This program can be stored in areadable storage medium of the computer, and the storage medium mayinclude ROM, RAM, disk or optical disk.

The above-mentioned content gives further detailed descriptions on thisdisclosure in combination with specific embodiments. The specificimplementation of this disclosure is not limited to these descriptions.For those skilled in the art, it is feasible to make several simpledeductions or substitutions without deviating from the concept of thisdisclosure.

1. A device for real-time recognition of restoration of spontaneouscirculation (ROSC) in a cardio-pulmonary resuscitation (CPR) process,comprising: a signal acquisition apparatus for acquiring pulse oximetrywaveform signals of a patient; and a signal analysis apparatus foranalyzing the signals to determine whether there is ROSC in the CPRprocess.
 2. The device of claim 1, wherein: the signal analysisapparatus comprises time-domain recognition logic and/orfrequency-domain recognition logic; the time-domain recognition logicdetermines whether there is ROSC in the CPR process by detecting a timeenvelope of the signals in the time domain, and the frequency-domainrecognition logic determines whether there is ROSC in the CPR process bydetecting time-varying features of spectral peaks of the signals in thefrequency domain.
 3. The device of claim 2, wherein: the time-domainrecognition logic determines that there is ROSC when continuous andregular envelope features are recognized.
 4. The device of claim 2,wherein: the frequency-domain recognition logic determines that there isROSC when spectral peaks are recognized continuously at differentfrequencies or significant amplitude change is recognized for spectralpeaks within a certain period.
 5. A restoration of spontaneouscirculation (ROSC) recognition and post-ROSC circulation qualityevaluation system, comprising an ROSC recognition device and a post-ROSCcirculation quality evaluation apparatus for evaluating post-ROSCcirculation quality; the ROSC recognition device comprises: a signalacquisition apparatus for acquiring pulse oximetry waveform signals of apatient; and a signal analysis apparatus for analyzing the signals todetermine whether there is ROSC in a CPR process.
 6. The ROSCrecognition and post-ROSC circulation quality evaluation system of claim5, wherein the post-ROSC circulation quality evaluation apparatusevaluates the post-ROSC circulation quality based on variations ofcardiac stroke volume within a certain period or based on variations ofpulse rate.
 7. The ROSC recognition and post-ROSC circulation qualityevaluation system of claim 6, wherein after the ROSC recognition devicehas determined there is ROSC, the post-ROSC circulation qualityevaluation apparatus calculates area characteristics of AC components ofpulse signals sampled by the ROSC recognition device to evaluate thestroke volume variations under spontaneous cardiac rhythm to reflectpost-ROSC circulation quality.
 8. An restoration of spontaneouscirculation (ROSC) feedback system in a cardio-pulmonary resuscitation(CPR) process, comprising: an ROSC recognition and post-ROSC circulationquality evaluation apparatus for real-time recognition of ROSC in theCPR process and for post-ROSC circulation quality evaluation; and a CPRapparatus for providing compression output to a patient; wherein, upondetection of the ROSC, the ROSC recognition and post-ROSC circulationquality evaluation apparatus controls the CPR apparatus to stopcompression output and start the post-ROSC circulation qualityevaluation; wherein, when the post-ROSC circulation quality is evaluatedto be unstable, the ROSC recognition and post-ROSC circulation qualityevaluation apparatus controls the CPR apparatus to restart thecompression output, and restarts the real-time recognition of ROSC. 9.The ROSC feedback system of claim 8, wherein further comprising: a CPRquality evaluation apparatus for evaluating CPR quality; wherein, whilethe CPR apparatus is providing the compression output, the CPR apparatusinteracts with the CPR quality evaluation apparatus so that the CPRquality evaluation apparatus recognizes CPR compression state andprovides feedback to the CPR apparatus to achieve an optimal compressionoutput.
 10. The ROSC feedback system of claim 8, wherein: the ROSCrecognition and post-ROSC circulation quality evaluation apparatuscomprises time-domain recognition logic and/or frequency-domainrecognition logic; the time-domain recognition logic determines whetherthere is ROSC in the CPR process by detecting time envelope features ofsampled signals in the time domain, and the frequency-domain recognitionlogic determines whether there is ROSC in the CPR process by detectingtime-varying features of spectral peaks of the sampled signals in thefrequency domain.
 11. The ROSC feedback system of claim 10, wherein: thetime-domain recognition logic determines that there is ROSC whencontinuous and regular envelope features are recognized.
 12. The ROSCfeedback system of claim 10, wherein: the frequency-domain recognitionlogic determines that there is ROSC when spectral peaks are recognizedcontinuously at different frequencies or significant amplitude change isrecognized for spectral peaks within a certain period.
 13. The ROSCfeedback system of claim 9, wherein: the ROSC recognition and post-ROSCcirculation quality evaluation apparatus comprises time-domainrecognition logic and/or frequency-domain recognition logic; thetime-domain recognition logic determines whether there is ROSC in theCPR process by detecting time envelope features of sampled signals inthe time domain, and the frequency-domain recognition logic determineswhether there is ROSC in the CPR process by detecting time-varyingfeatures of spectral peaks of the sampled signals in the frequencydomain.
 14. The ROSC feedback system of claim 13, wherein: thetime-domain recognition logic determines that there is ROSC whencontinuous and regular envelope features are recognized; thefrequency-domain recognition logic determines that there is ROSC whenspectral peaks are recognized continuously at different frequencies orsignificant amplitude change is recognized for spectral peaks within acertain period.
 15. An ROSC recognition device without externalcompression after defibrillation, comprising: a signal acquisitiondevice for acquiring pulse wave signals of a patient; and a signalanalysis device for analyzing the signals to determine whether there isROSC after defibrillation.
 16. The ROSC recognition device of claim 15,wherein: the signal analysis apparatus performs real-time filtering onthe signals by a band pass filter before analysis to eliminate noiseinterference beyond a physiological frequency band.
 17. The ROSCrecognition device of claim 15, wherein: the signal analysis apparatusestablishes a sliding time window for the signals, and then determineswhether there is a pulse feature within the sliding time window.
 18. TheROSC recognition device of claim 17, wherein the signal analysisapparatus determines that there is ROSC after defibrillation when apulse wave number in the sliding time window exceeds a threshold number,and the quality of a single pulse wave exceeds a threshold quality. 19.The ROSC recognition device of claim 18, wherein the quality of thesingle pulse wave is determined by its amplitude, width and shape. 20.The ROSC recognition device of claim 17, wherein a time duration of thesliding time window is adaptively adjusted according to frequencies ofthe pulse wave.